Endometriosis and polycystic ovary syndrome (PCOS) are common, multifactorial gynecological disorders shaped by endocrine imbalance, immune dysfunction, metabolic disruption, genetic susceptibility, and environmental exposures. Despite their major contribution to infertility and long-term cardiometabolic morbidity, early detection remains poor because symptoms are nonspecific, phenotypes are heterogeneous, and diagnosis is still dominated by single-modality and symptom-driven pathways. This review addresses this gap by synthesizing 2015-2025 evidence on shared and disease-specific biological mechanisms and evaluating how artificial intelligence (AI) can improve scalable screening and risk stratification. A narrative and integrative methodology was applied using peer-reviewed studies retrieved from PubMed, Scopus, Web of Science, and Google Scholar, emphasizing diagnostic rigor and external validity. Key findings identify convergent pathways involving chronic low-grade inflammation, adipokine dysregulation, oxidative stress, microbiome-mediated estrogen signaling, ferroptosis-linked iron imbalance, mitochondrial dysfunction, and epigenetic regulation through microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Promising early-detection signals include age-stratified anti-Müllerian hormone (AMH) thresholds, circulating cell-free deoxyribonucleic acid (cfDNA) methylation markers, and reproductive tract microbial signatures. AI-based models, including transformer architectures and multimodal machine learning, show strong potential to integrate clinical, hormonal, imaging, omics, and digital symptom phenotyping into reproducible early screening frameworks. Clinical translation requires standardized diagnostic definitions, longitudinal multi-ethnic cohorts, explainable algorithms, and prospective validation. AI-enabled precision screening offers a practical pathway to shorten diagnostic delay and improve reproductive outcomes.
Gupta et al. (Sat,) studied this question.